The Relationship Between Gambling Involvement and Financial Health: An Analysis of 1.8 Million UK Open Banking Customers
Session Title
Financial Impacts: Debt, Spending & Behavior
Presentation Type
Paper Presentation
Start Date
28-5-2026 12:00 AM
Abstract
This study utilizes a massive Open Banking dataset from 1.8 million UK customers to analyze the relationship between gambling and financial health over a 24-month period (Jan 2023 – Dec 2024). Unlike prior cross-sectional snapshots, this research employs a longitudinal design to track the stability of gambling behaviors and their impact on objective financial markers like debt service and savings. The study addresses five core objectives: (1) determining gambling prevalence; (2) examining sociodemographic variations; (3) exploring the correlation between gambling intensity and financial health; (4) identifying "highly-involved" subgroups using Gini coefficients and Lorenz curves; and (5) assessing the temporal persistence of high-risk behaviors. We hypothesize a heavy-tailed distribution where approximately 3% of users account for disproportionate activity and exhibit persistent engagement over time. Furthermore, we anticipate higher gambling involvement will correlate with increased financial distress. These findings offer critical empirical evidence for defining affordability thresholds and developing financial health indexes for harm minimization.
The Relationship Between Gambling Involvement and Financial Health: An Analysis of 1.8 Million UK Open Banking Customers
This study utilizes a massive Open Banking dataset from 1.8 million UK customers to analyze the relationship between gambling and financial health over a 24-month period (Jan 2023 – Dec 2024). Unlike prior cross-sectional snapshots, this research employs a longitudinal design to track the stability of gambling behaviors and their impact on objective financial markers like debt service and savings. The study addresses five core objectives: (1) determining gambling prevalence; (2) examining sociodemographic variations; (3) exploring the correlation between gambling intensity and financial health; (4) identifying "highly-involved" subgroups using Gini coefficients and Lorenz curves; and (5) assessing the temporal persistence of high-risk behaviors. We hypothesize a heavy-tailed distribution where approximately 3% of users account for disproportionate activity and exhibit persistent engagement over time. Furthermore, we anticipate higher gambling involvement will correlate with increased financial distress. These findings offer critical empirical evidence for defining affordability thresholds and developing financial health indexes for harm minimization.